Enhancing User Experience: Building RAG-Based LLM Applications

Enhancing User Experience: Building RAG-Based LLM Applications

Table of Contents

  1. Introduction
  2. Building the RAG Application
  3. The Value of Underlying Documents and User Questions
  4. Starting Simple: testing Base LM Performance
  5. The RAG Application Overview
  6. Chunking Strategies for Data Sources
  7. Experimenting with Chunking Techniques
  8. Choosing an Embedding Model
  9. Creating a Vector Database
  10. Exploring Different Vector Databases
  11. Pros and Cons of Using Postgres for Vector DB
  12. Evaluating Model Performance
  13. Incorporating Context and LLMS
  14. Classifier Training for LM Routing
  15. Cost Analysis of Different Models
  16. The Future of Routing Applications
  17. Conclusion

📰 Building an LM Application with RAG

Building an LM application has become a crucial aspect for many tech companies in enhancing the user experience of their products. In this article, we will explore how we built a RAG (Retrieve, Analyze, Generate) application at our company, with a focus on the underlying documents and user questions. We will also delve into the various strategies and techniques employed, including chunking, embedding models, vector databases, and LM routing. Additionally, we will analyze the cost and performance of different models and discuss the future of routing applications.

1️⃣ Introduction

In recent times, there has been a shift in focus from infrastructure optimization to building LM applications to improve the overall experience of products. By directly engaging with the development process, companies gain invaluable insights into the challenges faced by users, helping them create more effective solutions. This article details the journey our company undertook to build a RAG application, leveraging the capabilities and functions of our Ray platform.

2️⃣ Building the RAG Application

The first step in this endeavor was to build a RAG (Retrieve, Analyze, Generate) application, which is a common use case for many tech teams. The goal was to make it easier for users to work with our products by providing a user-friendly and efficient documentation system. The RAG application allows developers to quickly access Relevant information and obtain accurate responses by leveraging the extensive capabilities of Ray.

3️⃣ The Value of Underlying Documents and User Questions

When building an LM application like RAG, two crucial components contribute to its success: the underlying documents and user questions. The documentation is a significant source of information and serves as a foundation for the application. Our dedicated documentation team, led by Angelina and others, put in immense effort to ensure the quality and relevance of these documents. Additionally, user questions provide valuable insights to cater to their needs and improve the application further.

4️⃣ Starting Simple: Testing Base LM Performance

In the initial stage, we aimed to evaluate the performance of the base LM without any additional enhancements. We utilized GPT-4 models like gpd4, llama, 7B, and 70B to generate responses to various queries and quickly realized their limitations. These models lacked context and were often outdated due to rapid advancements in the LM space. This led us to explore and build the RAG app for augmented responses based on user queries.

5️⃣ The RAG Application Overview

The RAG application workflow involves multiple steps to generate accurate responses. First, the query is embedded using an embedding model, with options available to choose from. Next, the embedded query is passed into a vector database, which can employ various distance calculation techniques. The query is then used to retrieve the top K relevant contexts from the vector database. The texts from both the retrieved contexts and the query are fed into the LM to augment the base model's capabilities. This augmented LM becomes the backbone for generating correct responses.

6️⃣ Chunking Strategies for Data Sources

When dealing with a diverse range of data sources, effective chunking strategies are crucial to optimize the application's performance. Randomly chunking the data is a common and simple approach. However, we discovered that this method was not as efficient as desired. Therefore, we explored alternative techniques, such as using sections of HTML documents. This approach offered more precise references and ensured logical chunking without disrupting the flow of information.

7️⃣ Experimenting with Chunking Techniques

Throughout the application development process, we aimed to keep the chunking techniques as generalizable as possible. While we primarily worked with our Ray documentation, we strived to create a template that could be utilized for different types of HTML documents. By analyzing the HTML structure, we could identify the most effective way to chunk the data. This flexibility allowed us to cater to a wide range of user scenarios efficiently.

8️⃣ Choosing an Embedding Model

Embedding models play a crucial role in the RAG application by enabling the semantic representation of the different data chunks. We experimented with different embedding models to determine their effectiveness in capturing the essence of the Texts. We found that the choice of embedding model greatly influenced the overall performance and the quality of the augmented responses. Ultimately, we opted for an open-source embedding model that provided satisfactory results for our use case.

9️⃣ Creating a Vector Database

In order to store and retrieve the necessary information for the RAG application, we needed a robust Vector database. We explored several options available in the market and observed a recent surge in the development of new databases. Given our team's familiarity and previous experience, we decided to use PostgreSQL as our Vector database. Although it may not be the most cutting-edge solution, it provided stable performance and had the necessary features for our application.

🔟 Exploring Different Vector Databases

While we chose PostgreSQL for our Vector database, it is worth mentioning that there are numerous alternatives available. New databases such as VV8, ChromaDB, and Elasticsearch have entered the market with specialized features tailored for LM applications. Depending on specific requirements, it is essential to evaluate and choose the database that aligns with your team's expertise and objectives.

1️⃣1️⃣ Pros and Cons of Using Postgres for Vector DB

Using Postgres as our Vector database presented several advantages and a few limitations. One of the main benefits was the ease of integration since we were already familiar with it and had existing data within the database. Additionally, Postgres had a valuable extension called PG vectors, which enabled the creation of a new data type specifically designed for vector storage. On the flip side, scalability became a concern with large-Scale document repositories. In such cases, alternative databases might be better suited to handle the load.

1️⃣2️⃣ Evaluating Model Performance

Ensuring the quality of generated responses was a critical aspect of our RAG application. To assess the models' performance, we conducted comprehensive evaluations using a combination of techniques. One method involved querying the models with known queries and comparing the generated responses to pre-established gold standards. Another strategy entailed training a classifier to predict which model would produce the best response for a given query. By collecting feedback and iterating on these evaluations, we established the most effective models for our application.

1️⃣3️⃣ Incorporating Context and LM

Contextual understanding plays a vital role in improving the accuracy and relevance of responses generated by the LM. Through experiments, we discovered that incorporating context had a significant positive impact on the overall performance of our RAG application. By providing relevant context from underlying documents and user queries, we enabled the LM to generate more appropriate responses. Additionally, we acknowledged the trend towards larger context windows in LM models, paving the way for more advancements in this area.

1️⃣4️⃣ Classifier Training for LM Routing

To enhance the performance of our routing application, we trained a classifier to determine which LM to use for a given query. By annotating a dataset and assigning the most suitable LM for each query, we generated labeled examples that served as training data for the classifier. We experimented with different techniques such as using spaCy and logistic regression with softmax. With the trained classifier, we could dynamically route queries to the most relevant LM, improving both efficiency and accuracy.

1️⃣5️⃣ Cost Analysis of Different Models

Optimizing cost while maintaining high-quality responses is a key consideration for any LM application. We performed a cost analysis comparing different models, including open-source and commercial options. While larger models like GPT-4 exhibited superior performance, they came at a significantly higher cost. Other models like Llama 70b and Three-Fy Turbo provided comparable results at a more affordable price. To strike a balance, we employed LM routing, utilizing the most cost-effective models without sacrificing performance.

1️⃣6️⃣ The Future of Routing Applications

The RAG application journey has provided valuable insights into the power of building effective routing applications. As technology continues to evolve, we can expect further advancements in LM models, vector databases, and routing strategies. The future may witness the integration of LM inference into the routing decision-making process, empowering applications to make more informed judgments. Additionally, the open-source community is actively contributing to the expansion of routing capabilities, presenting exciting opportunities for innovation.

1️⃣7️⃣ Conclusion

Building a powerful and efficient LM application like RAG requires careful consideration of various components and strategies. Chunking, embedding models, vector databases, and LM routing all play crucial roles in the application's overall performance and user experience. By incorporating context and continuously iterating based on evaluations, developers can create an application that provides accurate, context-aware responses. The future holds tremendous potential for routing applications, with advancements in technology and a collaborative open-source community driving innovation.


Highlights:

  • Building an LM application using the RAG (Retrieve, Analyze, Generate) approach
  • Importance of underlying documents and user questions
  • Testing base LM performance and the need for augmentation
  • Overview of the RAG application workflow
  • Strategies for effective chunking of data sources
  • Experimenting with embedding models and vector databases
  • Pros and cons of using Postgres as a Vector DB
  • Evaluation of model performance and LM routing
  • Cost analysis of different models
  • Future advancements and possibilities for routing applications

FAQ:

Q: How important are underlying documents and user questions in building an LM application? A: Underlying documents serve as the foundation for the application, while user questions provide valuable insights and improve user experience.

Q: What is the RAG application workflow? A: The RAG application involves embedding queries, retrieving relevant context from a vector database, augmenting the base LM with context, and generating accurate responses.

Q: Are there any limitations to using Postgres as a Vector DB? A: Postgres is a reliable option, but it may not be suitable for large-scale document repositories. Other databases might offer better scalability.

Q: How can LM routing improve performance and efficiency? A: By training a classifier to determine the most suitable LM for a given query, LM routing ensures efficient and accurate responses.

Q: How can cost be optimized in LM applications? A: Evaluating the cost and performance of different models and employing LM routing can help strike a balance between cost and quality.


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